Downloads

https://doi.org/10.62836/jitp.2025.515
Kaptosv, L. (2025). RESTful API Design for Geospatial Logistics Platforms Using Type Script and Laravel. Journal of Information, Technology and Policy, 1–13. https://doi.org/10.62836/jitp.2025.515
Volume 3, Issue 1, 2025
Copyright (c) 2025 Liubomyr Kaptosv
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

RESTful API Design for Geospatial Logistics Platforms Using Type Script and Laravel

The growing demands of logistics and supply chain management have created an urgent need for scalable, high-performance, and secure geospatial platforms capable of handling real-time data exchange. RESTful APIs have become one of the fundamental architectural solutions, as their implementation enables interoperability, modular application design, and cross-platform integration within logistics ecosystems. This research paper presents the data structure and implementation of a geospatial logistics platform based on RESTful API architecture, developed using TypeScript and Laravel. The proposed system leverages the strong typing and asynchronous processing capabilities of TypeScript, which enhance maintainability and error handling, while Laravel provides a robust backend framework for orchestrating APIs, managing authorization, and manipulating data. A relational architectural design was adopted to ensure scalability, and geospatial functionality was integrated through mapping libraries and spatial databases. The platform was evaluated using performance benchmarks, analyses of API response times, and developer surveys. The results demonstrate a significant reduction in response latency, increased request throughput, and lower error rates compared with traditional PHP-based REST frameworks. Moreover, the use of TypeScript scripts streamlined the development process, making the codebase less cumbersome and easier to maintain. The findings highlight the potential of combining modern typed programming languages with established backend frameworks to address the challenges of geospatial logistics platforms, particularly in improving the accuracy of real-time geospatial data, optimizing routes, and enhancing system scalability. This study contributes to the growing body of literature on logistics software architectures and provides practical guidance for future implementations of geospatial APIs.

supply chain technology type script geospatial logistics laravel scalable architecture real-time data

References

  1. Sunyaev A. Applications and Systems Integration. In Internet Computing; Springer: Cham, Switzerland, 2024. https://doi.org/10.1007/978-3-031-61014-1_5.
  2. Kotstein S, Decker C. RESTBERTa: A Transformer-Based Question Answering Approach for Semantic Search in Web API Documentation. Cluster Computing 2024; 27: 4035–4061. https://doi.org/10.1007/s10586-023-04237-x.
  3. Haupt F, Leymann F, Vukojevic-Haupt K. API Governance Support through the Structural Analysis of REST APIs. Computer Science—Research and Development 2017; 33(3–4): 291–303. https://doi.org/10.1007/s00450-017-0384-1.
  4. Gamez-Diaz A, Fernandez P, Ruiz-Cortes A. An Analysis of RESTful APIs Offerings in the Industry. In Service-Oriented Computing; Springer International Publishing: Cham, Switzerland, 2017; pp. 589–604. https://doi.org/10.1007/978-3-319-69035-3_43.
  5. Kulesza R, de Sousa MF, de Araújo MLM, et al. Evolution of Web Systems Architectures: A Roadmap. In Special Topics in Multimedia, IoT and Web Technologies; Springer International Publishing: Cham, Switzerland, 2020; pp. 3–21. https://doi.org/10.1007/978-3-030-35102-1_1.
  6. Bogner J, Wagner S, Zimmermann A. Collecting Service-Based Maintainability Metrics from RESTful API Descriptions: Static Analysis and Threshold Derivation. In Communications in Computer and Information Science; Springer International Publishing: Cham, Switzerland, 2020; pp. 215–227. https://doi.org/10.1007/978-3-030-59155-7_16.
  7. Palma F, Gonzalez-Huerta J, Moha N, et al. Are RESTful APIs Well-Designed? Detection of their Linguistic (Anti)Patterns. In Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2015; pp. 171–187. https://doi.org/10.1007/978-3-662-48616-0_11.
  8. Baresi L, Garriga M. Microservices: The Evolution and Extinction of Web Services? In Microservices; Springer International Publishing: Cham, Switzerland, 2020; pp. 3–28. https://doi.org/10.1007/978-3-030-31646-4_1.
  9. Wang S, Zhong Y, Wang E. An Integrated GIS Platform Architecture for Spatiotemporal Big Data. Future Generations Computer Systems 2019; 94: 160–172. https://doi.org/10.1016/j.future.2018.10.034.
  10. Sun K, Zhu Y, Pan P, et al. Geospatial Data Ontology: The Semantic Foundation of Geospatial Data Integration and Sharing. Big Earth Data 2019; 3(3): 269–296. https://doi.org/10.1080/20964471.2019.1661662.
  11. Reider R, Mukku VD, Müller M, et al. Design of a Data Integration and Exchange Service for Touristic City and Logistics Planning in Şanlıurfa, Turkey. Procedia Computer Science 2025; 253: 3000–3006. https://doi.org/10.1016/j.procs.2025.02.024.
  12. Bill R, Blankenbach J, Breunig M, et al. Geospatial Information Research: State of the Art, Case Studies and Future Perspectives. PFG—Journal of Photogrammetry Remote Sensing and Geoinformation Science 2022; 90(4): 349–389. https://doi.org/10.1007/s41064-022-00217-9.
  13. Huang L-Y, Li S-Y, Zou X, et al. Knowledge-Driven Logistics Transformation: Complex Networks and UAVs in Distribution. Journal of the Knowledge Economy 2024; 16(1): 1583–1622. https://doi.org/10.1007/s13132-024-01984-z.
  14. Greenough PG, Nelson EL. Beyond Mapping: A Case for Geospatial Analytics in Humanitarian Health. Conflict and Health 2019; 13(1): 50. https://doi.org/10.1186/s13031-019-0234-9.
  15. Feng B, Ye Q. Operations Management of Smart Logistics: A Literature Review and Future Research. Frontiers of Engineering Management 2021; 8(3): 344–355. https://doi.org/10.1007/s42524-021-0156-2.
  16. Chen X. The Development Trend and Practical Innovation of Smart Cities under the Integration of New Technologies. Frontiers of Engineering Management 2019; 6(4): 485–502. https://doi.org/10.1007/s42524-019-0057-9.
  17. Ono K, Fong D, Gao C, et al. Cytoscape Web: Bringing network biology to the browser. Nucleic Acids Research 2025; 53(W1): W203–W212. https://doi.org/10.1093/nar/gkaf365.
  18. Vedhapriyavadhana R, Bharti P, Chidambaranathan S. Detecting Dark Patterns in Shopping Websites—A Multi-Faceted Approach Using Bidirectional Encoder Representations from Transformers (BERT). Enterprise Information Systems 2025; 19(5–6). https://doi.org/10.1080/17517575.2025.2457961.
  19. Đorđević A, Stefanovic M, Petrović T, et al. JavaScript MEAN Stack Application Approach for Real-Time Nonconformity Management in SMEs as a Quality Control Aspect within Industry 4.0 Concept. International Journal of Computer Integrated Manufacturing 2024; 37(5): 630–651. https://doi.org/10.1080/0951192x.2023.2228274.
  20. bin Uzayr S. Mastering NativeScript: A Beginner’s Guide; CRC Press: Boca Raton, FL, USA, 2022. https://doi.org/10.1201/9781003299394.
  21. Orłowski C, Cygert D, Nowak P. Extended Continuous Improvement Model for Internet of Things System Design Environments. Journal of Information and Telecommunication 2021; 5(3): 279–295. https://doi.org/10.1080/24751839.2020.1847506.
  22. Kosicki M, Tsigkari M, Borgstrom O, et al. Urban configurations: Mass Optimization with Real-World Constraints Using High-Performance Computing. Technology|Architecture + Design 2025; 9(1): 34–46. https://doi.org/10.1080/24751448.2025.2465070.
  23. De Maria A, Bodin M, Gaonach M, et al. DRAC—Data Repository for Advancing Open sCience. Synchrotron Radiation News 2024; 37(6): 35–42. https://doi.org/10.1080/08940886.2024.2432816.
  24. Miah MO, Kong J. Augmented Reality and Cross-Device Interaction for Seamless Integration of Physical and Digital Scientific Papers. International Journal of Human–Computer Interaction 2024; 41(11): 7040–7057. https://doi.org/10.1080/10447318.2024.2388372.
  25. Kumaresan A, Liberona D, Gnanamurthy RK. A Case Study on API-Centric Big Data Architecture. In Communications in Computer and Information Science; Springer International Publishing: Cham, Switzerland, 2017; pp. 459–469. https://doi.org/10.1007/978-3-319-62698-7_38.
  26. Meroño-Peñuela A, Hoekstra R. Automatic Query-Centric API for Routine Access to Linked Data. In Lecture Notes in Computer Science; Springer International Publishing: Cham, Switzerland, 2017; pp. 334–349. https://doi.org/10.1007/978-3-319-68204-4_30.
  27. Seuri O, Ikäheimo H-P, Huhtamäki J. What Happens When Platforms Mediate the Audience–Journalism Relationship? In Futures of Journalism; Springer International Publishing: Cham, Switzerland, 2022; pp. 227–243. https://doi.org/10.1007/978-3-030-95073-6_15.
  28. Farias RS, de Souza RM, McGregor JD, et al. Designing Smart City Mobile Applications: An Initial Grounded Theory. Empirical Software Engineer 2019; 24(6): 3255–3289. https://doi.org/10.1007/s10664-019-09723-8.
  29. Zurita Macías JE, Almeida Arlucea A. Integrative cloud to mist computing: Architectures, applications, and innovations in data engineering. In Engineering Cyber-Physical Systems and Critical Infrastructures; Springer Nature: Cham, Switzerland, 2025; pp. 159–182. https://doi.org/10.1007/978-3-031-83149-2_8.
  30. Rathee S, Chobe A. Open Source in Infrastructure. In Getting Started with Open Source Technologies; Apress: New York, NY, USA, 2022; pp. 75–98. https://doi.org/10.1007/978-1-4842-8127-7_5.
  31. Bhatele A, Brink S, Gamblin T. Hatchet: Pruning the Overgrowth in Parallel Profiles. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC ‘19), Denver, CO, USA, 17–19 November 2019; pp. 1–21. https://doi.org/10.1145/3295500.3356219.
  32. Xie Y, Lin J, Dong H, et al. Survey of Code Search Based on Deep Learning. ACM Transactions on Software Engineering and Methodology 2024; 33(2): 1–42. https://doi.org/10.1145/3628161.
  33. Iadanza C, Trigila A, Starace P, et al. IdroGEO: A Collaborative Web Mapping Application Based on REST API Services and Open Data on Landslides and Floods in Italy. ISPRS International Journal of Geo-Information 2021; 10(2): 89. https://doi.org/10.3390/ijgi10020089.
  34. Pakdil ME, Çelik RN. Serverless Geospatial Data Processing Workflow System Design. ISPRS International Journal of Geo-Information 2021; 11(1): 20. https://doi.org/10.3390/ijgi11010020.
  35. Niroshan L, Moslem S, Pilla F. Design and Implementation of a Data Sharing API for Supporting Urban Governance Schemes in Environmental and Traffic Monitoring. MethodsX 2025; 15(103458): 103458. https://doi.org/10.1016/j.mex.2025.103458.
  36. Al-Yadumi S, Xion TE, Wei SGW, et al. Review on Integrating Geospatial Big Datasets and Open Research Issues. IEEE Access: Practical Innovations, Open Solutions 2021; 9: 10604–10620. https://doi.org/10.1109/access.2021.3051084.
  37. Kraft R, Birk F, Reichert M, et al. Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform. Sensors 2020; 20(12): 3456. https://doi.org/10.3390/s20123456.
  38. Brunetti M, Mes M, Lalla-Ruiz E. Smart Logistics Nodes: Concept and Classification. International Journal of Logistics Research and Applications 2024; 27(11): 1984–2020. https://doi.org/10.1080/13675567.2024.2327394.
  39. Guo D, Onstein E. State-of-the-Art Geospatial Information Processing in NoSQL Databases. ISPRS International Journal of Geo-Information 2020; 9(5): 331. https://doi.org/10.3390/ijgi9050331.
  40. Ehsan A, Abuhaliqa MAME, Catal C, et al. RESTful API Testing Methodologies: Rationale, Challenges, and Solution Directions. Applied Sciences 2022; 12(9): 4369. https://doi.org/10.3390/app12094369.
  41. Rieke M, Bigagli L, Herle S, et al. Geospatial IoT—The Need for Event-Driven Architectures in Contemporary Spatial Data Infrastructures. ISPRS International Journal of Geo-Information 2018; 7(10): 385. https://doi.org/10.3390/ijgi7100385.
  42. Qin Z, Wang G, Deng W, et al. Supporting Technology of E-Commerce. In E-Commerce in Theory and Practice; Springer Nature: Singapore, 2024; pp. 173–272. Available online: https://content.e-bookshelf.de/media/reading/L-24668877-9cb870148f.pdf (accessed on 7 September 2025).
  43. Denissova N, Petrova O, Mashayev E, et al. Real-Time Avalanche Hazard Monitoring System Based on Weather Sensors and a Laser Rangefinder. Sensors 2025; 25(9): 2937. https://doi.org/10.3390/s25092937.
  44. Wang J, Mo F, Qiao S, et al. Spatial Computing in Digital Twins. Digital Twin 2025; 2(2). https://doi.org/10.1080/27525783.2025.2508268.
  45. Yang H, Kumara S, Bukkapatnam STS, et al. The Internet of Things for Smart Manufacturing: A Review. IISE Transactions 2019; 51(11): 1190–1216. https://doi.org/10.1080/24725854.2018.1555383.
  46. Ali IA, Bukhari WA, Adnan M, et al. Security and Privacy in IoT-Based Smart Farming: A review. Multimedia Tools and Applications 2024; 84(16): 15971–16031. https://doi.org/10.1007/s11042-024-19653-3.
  47. Zhang X, Balaji MS, Jiang Y. Charting the Future of Digital Servitization Using CIMO Framework. International Studies of Management & Organization 2024; 55(1): 112–139. https://doi.org/10.1080/00208825.2024.2372461.
  48. Aguiar-Castillo L, Guerra V, Rufo J, et al. Survey on Optical Wireless Communications-Based Services Applied to the Tourism Industry: Potentials and Challenges. Sensors 2021; 21(18): 6282. https://doi.org/10.3390/s21186282.
  49. Palanisamy S, Thangaraju V, Kandasamy J, et al. Towards Precision in IoT-Based Healthcare Systems: A Hybrid Optimized Framework for Big Data Classification. Journal of Big Data 2025; 12(1). https://doi.org/10.1186/s40537-025-01243-1.
  50. Kumar A, Yadav JP, Maheshwari S, et al. Revolutionizing Healthcare with 5G and AI: Integrating Emerging Technologies for Personalized Care and Cancer Management. Intelligent Hospital 2025; 1(1): 100005. https://doi.org/10.1016/j.inhs.2025.100005.
  51. Prabha BD, Akilashri PSS. An Intelligent Dashboard Framework for Healthcare Data Analysis and Disease Outbreak Prediction. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2025; 13(1). https://doi.org/10.1080/21681163.2025.2500433.

Supporting Agencies

  1. Funding: This research received no external funding.